The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation

Mortensen, Anders Krogh; Skovsen, Søren; Karstoft, Henrik; Gislum, René

doi:10.1109/cvprw.2019.00328

Cited by 10 publications

(5 citation statements)

References 10 publications

(13 reference statements)

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“…With an improved detection accuracy, subtle stress differences among cultivars/treatments may be revealed to enhance our understanding of plant responses to stresses. Therefore, some studies explored the use of CNNs for plant segmentation [59][60][61][62][63][64]. Most of them considered plant seg-mentation a semantic segmentation task and used encoderdecoder-based CNN architecture for processing.…”

Section: Cnn-based Analytical Approaches Formentioning

confidence: 99%

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

2020

Plant Phenomics

232

180

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Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

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Section: Cnn-based Analytical Approaches Formentioning

confidence: 99%

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

2020

Plant Phenomics

232

180

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“…We can detect plant disease, type of plant, the water content in the plant, and flowering of plants, and take necessary steps if a problem arises (DeChant et al, 2017;Ghosal et al, 2019;Arya et al, 2022). Recently, scientists have started applying deep learning techniques to plant phenotyping studies (Almahairi et al, 2018;Ghosal et al, 2019;Mortensen et al, 2019). Deep learning models can analyze large amounts of data, find previously thought impossible features, and do all these more accurately than ever before.…”

Section: Explainable Ai and Plant Phenotypingmentioning

confidence: 99%

“…However, another interesting application of segmentation is to generate annotated datasets. An oil radish growth dataset was presented by Mortensen et al ( 2019 ), which contained images of oil radish collected over weeks. In the study, the authors used the fully connected neural network proposed by Long et al ( 2015 ) for semantic segmentation of the oil radish and other plants and achieved 71.2% mean intersection over union (mIoU).…”

Section: Explainable Ai and Plant Phenotypingmentioning

confidence: 99%

Explainable deep learning in plant phenotyping

Mostafa,

Mondal,

Panjvani

et al. 2023

Front. Artif. Intell.

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The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.

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“…Counting in agriculture Guy Farjon and Yael Edan Target-Object Data Type Count Center-Dot Detection Segmentation Fish 1 [8] Videos ---Grape clusters 2 [74] RGB Images --Apple flowers 3 [60] RGB Images ---Apples 3 [60] RGB Images ---Apples 4 [89] RGB Images ---Apples 5 [50] RGB Images --Mangoes 5 [50] RGB Images ---Almonds 5 [50] RGB Images ---Grape clusters 6 [90] RGB Images ---Pigs 7 [20] RGB Images --Cattle 8 [91] RGB Images ---Oil Palm 9 [92] RGB Images ---Acacia 9 [92] RGB Images ---Wheat 10 [93] RGB Images ---Oil Radish 11 [94] RGB Images ---Plant seedlings 12 [95] Videos + RGB Images ---Apples 13 [96] RGB-DS ---Mango 14 [97] RGB Images ---Apple 15 [98] RGB Images + SfM --Vegtable (various) [99] 16 RGB Images ---Table 1: Publicly available datasets as collected from the 243 reviewed papers. The annotations available from each dataset along with a link to download the data is presented.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Farjon¹,

Liu²,

Edan³

2023

Preprint

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The number of objects is considered an important factor in a variety of tasks in the agricultural domain. Automated counting can improve farmers' decisions regarding yield estimation, stress detection, disease prevention, and more. In recent years, deep learning has been increasingly applied to many agriculture-related applications, complementing conventional computer-vision algorithms for counting agricultural objects. This article reviews progress in the past decade and the state of the art for counting methods in agriculture, focusing on deep-learning methods. It presents an overview of counting algorithms, metrics, platforms, and sensors, a list of all publicly available datasets, and an in-depth discussion of various deep-learning methods used for counting. Finally, it discusses open challenges in object counting using deep learning and gives a glimpse into new directions and future perspectives for counting research. The review reveals a major leap forward in object counting in agriculture in the past decade, led by the penetration of deep learning methods into counting platforms.

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The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation

Cited by 10 publications

References 10 publications

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

Explainable deep learning in plant phenotyping

Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

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